A network robustness evaluation method and system

Abstract

Embodiments of the invention disclose a method and system for network robustness evaluation. The method includes the steps of acquiring initial parameters of a network and acquiring an initial load model of the network according to the initial parameters, the initial parameters including an initial number of nodes, an initial number of edges, an initial number of random walkers, a load tolerance parameter, and a load reduction parameter of the network; simulating a random walk-based network fault in the network, and acquiring, based on the initial parameters and the initial load model and according to a preset rule, the size of a corresponding target network giant component when the network is stable after the network fault; and evaluating the robustness of the network according to the size of the target network giant component. The system is used to perform the above method. According to the embodiments of the invention, the speed and accuracy of the network robustness evaluation are increased.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of reliability engineering, and in particular to a network robustness evaluation method and system. Background technique [0002] A network is made up of nodes and links, representing objects and their interrelationships. There are many complex networks in life, such as: the power network is a network composed of power stations and cables connecting power stations; the social network is a network composed of social people and the relationship between people; the traffic network is a road between intersections and intersections Networks that form together; neural networks, computer networks, the Internet of Things, etc. are all similar networks. These networks are often open, that is, the nodes and edges in the network interact with the external environment. When a small disturbance occurs in the external environment and causes several nodes in the network to fail, it may be due to node...

AI Technical Summary

Problems solved by technology

However, it is necessary to know the connections of all nodes in the network, which is not possible in practice, and requires a relatively large amount of data, and the data processing process is complicated, which affects the speed and accuracy of network robustness evaluation.

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